Flexible pavement analysis using physics-informed machine learning methods

Zhou, Qingwen

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https://hdl.handle.net/2142/122251 Copy

Description

Title

Flexible pavement analysis using physics-informed machine learning methods

Author(s)

Zhou, Qingwen

Issue Date

2023-11-30

Director of Research (if dissertation) or Advisor (if thesis)

Al-Qadi, Imad

Doctoral Committee Chair(s)

Al-Qadi, Imad

Committee Member(s)

• Ouyang, Yanfeng
• Talebpour, Alireza
• Tsai, Yi-Chang
• Garg, Navneet

Department of Study

Civil & Environmental Eng

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

Pavement analysis, physics-informed machine learning, pavement structural responses, 3-dimensional finite element analysis, graph neural networks

Abstract

According to US Transportation Statistics in 2023, over 3.2 trillion vehicle miles were traveled in 2021, while a concerning 43% of public roadways exhibited poor to mediocre conditions. This decline in roadway quality translates to a staggering financial toll on motorists, approximating an annual cost of $130 billion in vehicle repairs and associated operational expenses. To enhance time and cost efficiencies, the engineering domain has witnessed an escalating emphasis on advanced computational techniques in pavement design and management. A primary challenge is the time and computer resources required to simulate pavement structure responses using three-dimensional (3D) finite element (FE) analyses. This study provides an accurate and efficient approach to predict pavement response using graph neural network (GNN) and optimal model to predict IRI over the pavement service life, using ANN, incorporating maintenance activities. This dissertation introduced the use of GNN to predict pavement responses utilizing an extensive FE analysis dataset. A database was created as a central resource for this study, containing 240 FE cases of the 819 3D FE simulations of flexible highway pavements conducted over two decades. The GNN was used to build pavement FE nodes into graph nodes, embedding physics-based interaction between nodes within its machine learning (ML) architecture. The dynamic behaviors of pavement FEs were computed via learned message-passing between two graphs within two continuous timesteps. This model achieved a one-step mean square error (MSE) as minuscule as 1.21×10^(-8) and a rollout MSE of approximately 4.55×10^(-5). This prediction framework, astoundingly efficient, requires a week of model training but only a mere 5 min of prediction for each single case, presenting a stark contrast to traditional 3D FE analyses that could span hours to weeks for a single case. A systematic exploration of hyperparameters accentuated the optimal configuration of 10 message-passing steps (M) and a single historical time step (C), balanced adeptly between model performance and computational expediency. Additionally, preliminary data normalization surfaced as a pivotal step, considerably attenuating simulation noise. The pre-trained GNN model, which was originally developed for highway pavement analysis, was adapted for airfield pavement structural responses. Using 11 3D FE simulation cases, 226 individual steps were utilized in analysis. Utilizing the strategies model scaling and graph pooling, satisfactory mean squared errors (MSE) of 1.24×10^(-7) and 2.26×10^(-7) were resulted in one-step predictions, respectively. The transfer learning approach further reduced 3D FE analysis of several weeks and the GNN model training process of one week to less than 2 hrs of model training and 10 min of simulation for each case. It was found that the model scaling might be susceptible to overfitting in the base layer, while graph pooling might introduce higher errors in the subbase and subgrade layers. This dissertation also delved into the domain of pavement management, particularly the International Roughness Index (IRI) prediction (IRI progression over the pavement’s service life without maintenance/rehabilitation and the drop in IRI after maintenance models). The first model utilizes the recurrent neural network (RNN) algorithm using data extracted from the Long-Term Pavement Performance (LTPP) database. A RNN based long short-term memory network (LSTM) was used to solve the vanishing gradient problem. The second model utilized an artificial neural network (ANN) algorithm to correlate the impacting factors to the IRI value after maintenance. Combining the two models allowed for the prediction of IRI values over AC pavement’s service life.

Graduation Semester

2023-12

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Qingwen Zhou

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